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Effect of Fixed and sEMG-Based Adaptive Shared Steering Control on Distracted Driver Behavior

Wang, Zheng ; Suga, Satoshi ; Nacpil, Edric John Cruz ; Yang, Bo ; Nakano, Kimihiko (2022)
Effect of Fixed and sEMG-Based Adaptive Shared Steering Control on Distracted Driver Behavior.
In: Sensors, 2022, 21 (22)
doi: 10.26083/tuprints-00020076
Artikel, Zweitveröffentlichung, Verlagsversion

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Kurzbeschreibung (Abstract)

Driver distraction is a well-known cause for traffic collisions worldwide. Studies have indicated that shared steering control, which actively provides haptic guidance torque on the steering wheel, effectively improves the performance of distracted drivers. Recently, adaptive shared steering control based on the forearm muscle activity of the driver has been developed, although its effect on distracted driver behavior remains unclear. To this end, a high-fidelity driving simulator experiment was conducted involving 18 participants performing double lane change tasks. The experimental conditions comprised two driver states: attentive and distracted. Under each condition, evaluations were performed on three types of haptic guidance: none (manual), fixed authority, and adaptive authority based on feedback from the forearm surface electromyography of the driver. Evaluation results indicated that, for both attentive and distracted drivers, haptic guidance with adaptive authority yielded lower driver workload and reduced lane departure risk than manual driving and fixed authority. Moreover, there was a tendency for distracted drivers to reduce grip strength on the steering wheel to follow the haptic guidance with fixed authority, resulting in a relatively shorter double lane change duration.

Typ des Eintrags: Artikel
Erschienen: 2022
Autor(en): Wang, Zheng ; Suga, Satoshi ; Nacpil, Edric John Cruz ; Yang, Bo ; Nakano, Kimihiko
Art des Eintrags: Zweitveröffentlichung
Titel: Effect of Fixed and sEMG-Based Adaptive Shared Steering Control on Distracted Driver Behavior
Sprache: Englisch
Publikationsjahr: 2022
Publikationsdatum der Erstveröffentlichung: 2022
Verlag: MDPI
Titel der Zeitschrift, Zeitung oder Schriftenreihe: Sensors
Jahrgang/Volume einer Zeitschrift: 21
(Heft-)Nummer: 22
Kollation: 14 Seiten
DOI: 10.26083/tuprints-00020076
URL / URN: https://tuprints.ulb.tu-darmstadt.de/20076
Zugehörige Links:
Herkunft: Zweitveröffentlichung DeepGreen
Kurzbeschreibung (Abstract):

Driver distraction is a well-known cause for traffic collisions worldwide. Studies have indicated that shared steering control, which actively provides haptic guidance torque on the steering wheel, effectively improves the performance of distracted drivers. Recently, adaptive shared steering control based on the forearm muscle activity of the driver has been developed, although its effect on distracted driver behavior remains unclear. To this end, a high-fidelity driving simulator experiment was conducted involving 18 participants performing double lane change tasks. The experimental conditions comprised two driver states: attentive and distracted. Under each condition, evaluations were performed on three types of haptic guidance: none (manual), fixed authority, and adaptive authority based on feedback from the forearm surface electromyography of the driver. Evaluation results indicated that, for both attentive and distracted drivers, haptic guidance with adaptive authority yielded lower driver workload and reduced lane departure risk than manual driving and fixed authority. Moreover, there was a tendency for distracted drivers to reduce grip strength on the steering wheel to follow the haptic guidance with fixed authority, resulting in a relatively shorter double lane change duration.

Freie Schlagworte: driver–automation shared control, haptic guidance steering, adaptive automation design, surface electromyography, driver distraction
Status: Verlagsversion
URN: urn:nbn:de:tuda-tuprints-200766
Sachgruppe der Dewey Dezimalklassifikatin (DDC): 600 Technik, Medizin, angewandte Wissenschaften > 600 Technik
600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau
Fachbereich(e)/-gebiet(e): 18 Fachbereich Elektrotechnik und Informationstechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik
Hinterlegungsdatum: 29 Apr 2022 09:04
Letzte Änderung: 02 Mai 2022 10:03
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